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Notes -
The most plausible reason for changing the tokenizer is that a more fine-grained tokenizer increases model performance, at the cost of more compute per token (we're breaking up the same input into more tokens). My understanding is that you don't even need a new base model to do this, and that the gains are particularly pronounced for arithmetic and coding. It's not a free lunch, but there are pros and cons that don't just amount to Anthropic nickle and diming their customers.
Anthropic is, by far, the most compute strapped frontier LLM company. They are also not the only frontier LLM company. Until at least Google and OAI engage in the same putative enshittification (which I am far from sure is even happening wrt Anthropic), then you're kinda jumping the gun here.
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